def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
k = dist.k
tau_e = dist.tau_e
return r'${} \sim \text{{AR1}}(\mathit{{k}}={}, \mathit{{tau_e}}={})$'.format(name,
get_variable_name(k), get_variable_name(tau_e))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
cov = dist.cov
return r'${} \sim \text{MvGaussianRandomWalk}(\mathit{{mu}}={}, \mathit{{cov}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(cov))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
sd = dist.sd
return r'${} \sim \text{{GaussianRandomWalk}}(\mathit{{mu}}={}, \mathit{{sd}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(sd))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
n = dist.n
p = dist.p
return r'${} \sim \text{{Multinomial}}(\mathit{{n}}={}, \mathit{{p}}={})$'.format(name,
get_variable_name(n),
get_variable_name(p))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
nu = dist.nu
V = dist.V
return r'${} \sim \text{{Wishart}}(\mathit{{nu}}={}, \mathit{{V}}={})$'.format(name,
get_variable_name(nu),
get_variable_name(V))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
theta = dist.theta
psi = dist.psi
return r'${} \sim \text{{ZeroInflatedPoisson}}(\mathit{{theta}}={}, \mathit{{psi}}={})$'.format(name,
get_variable_name(theta),
get_variable_name(psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
lower = dist.lower
upper = dist.upper
return r'${} \sim \text{{DiscreteUniform}}(\mathit{{lower}}={}, \mathit{{upper}}={})$'.format(name,
get_variable_name(lower),
get_variable_name(upper))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
name_eta = get_variable_name(dist.eta)
name_cutpoints = get_variable_name(dist.cutpoints)
return (r'${} \sim \text{{OrderedLogistic}}'
r'(\mathit{{eta}}={}, \mathit{{cutpoints}}={}$'
.format(name, name_eta, name_cutpoints))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
alpha = dist.alpha
return r'${} \sim \text{{NegativeBinomial}}(\mathit{{mu}}={}, \mathit{{alpha}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(alpha))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
q = dist.q
beta = dist.beta
return r'${} \sim \text{{DiscreteWeibull}}(\mathit{{q}}={}, \mathit{{beta}}={})$'.format(name,
get_variable_name(q),
get_variable_name(beta))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
n = dist.n
p = dist.p
psi = dist.psi
return r'${} \sim \text{{ZeroInflatedBinomial}}(\mathit{{n}}={}, \mathit{{p}}={}, \mathit{{psi}}={})$'.format(
name, get_variable_name(n), get_variable_name(p),
get_variable_name(psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
nu = dist.nu
mu = dist.mu
cov = dist.cov
return r'${} \sim \text{MvStudentTRandomWalk}(\mathit{{nu}}={}, \mathit{{mu}}={}, \mathit{{cov}}={})$'.format(
name, get_variable_name(nu), get_variable_name(mu),
get_variable_name(cov))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
lower = dist.lower
upper = dist.upper
name = r'\text{%s}' % name
return r'${} \sim \text{{DiscreteUniform}}(\mathit{{lower}}={},~\mathit{{upper}}={})$'.format(name,
get_variable_name(lower),
get_variable_name(upper))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
w = dist.w
sd = dist.sd
return r'${} \sim \text{{NormalMixture}}(\mathit{{w}}={}, \mathit{{mu}}={}, \mathit{{sigma}}={})$'.format(
name, get_variable_name(w), get_variable_name(mu),
get_variable_name(sd))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
omega = dist.omega
alpha_1 = dist.alpha_1
beta_1 = dist.beta_1
return r'${} \sim \text{GARCH}(1, 1, \mathit{{omega}}={}, \mathit{{alpha_1}}={}, \mathit{{beta_1}}={})$'.format(
name, get_variable_name(omega), get_variable_name(alpha_1),
get_variable_name(beta_1))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
alpha = dist.alpha
beta = dist.beta
name = r'\text{%s}' % name
return r'${} \sim \text{{BetaBinomial}}(\mathit{{alpha}}={},~\mathit{{beta}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(beta))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
alpha = dist.alpha
psi = dist.psi
return r'${} \sim \text{{ZeroInflatedNegativeBinomial}}(\mathit{{mu}}={}, \mathit{{alpha}}={}, \mathit{{psi}}={})$'.format(
name, get_variable_name(mu), get_variable_name(alpha),
get_variable_name(psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
n = dist.n
p = dist.p
name = r'\text{%s}' % name
return r'${} \sim \text{{Binomial}}(\mathit{{n}}={},~\mathit{{p}}={})$'.format(name,
get_variable_name(n),
get_variable_name(p))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
alpha = dist.alpha
beta = dist.beta
name = r'\text{%s}' % name
return r'${} \sim \text{{NegativeBinomial}}(\mathit{{alpha}}={},~\mathit{{beta}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(beta))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
theta = dist.theta
psi = dist.psi
name = r'\text{%s}' % name
return r'${} \sim \text{{ZeroInflatedPoisson}}(\mathit{{theta}}={},~\mathit{{psi}}={})$'.format(name,
get_variable_name(theta),
get_variable_name(psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
alpha = dist.alpha
name = r'\text{%s}' % name
return r'${} \sim \text{{NegativeBinomial}}(\mathit{{mu}}={},~\mathit{{alpha}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(alpha))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
sigma = dist.sigma
name = r'\text{%s}' % name
return r'${} \sim \text{{GaussianRandomWalk}}(\mathit{{mu}}={},~\mathit{{sigma}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(sigma))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
q = dist.q
beta = dist.beta
name = r'\text{%s}' % name
return r'${} \sim \text{{DiscreteWeibull}}(\mathit{{q}}={},~\mathit{{beta}}={})$'.format(name,
get_variable_name(q),
get_variable_name(beta))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
sigma = dist.sigma
name = r'\text{%s}' % name
return r'${} \sim \text{{GaussianRandomWalk}}(\mathit{{mu}}={},~\mathit{{sigma}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(sigma))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.innov.mu
cov = dist.innov.cov
name = r'\text{%s}' % name
return r'${} \sim \text{MvGaussianRandomWalk}(\mathit{{mu}}={},~\mathit{{cov}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(cov))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
nu = dist.nu
Sigma = dist.Sigma
return r'${} \sim \text{{MvStudentT}}(\mathit{{nu}}={}, \mathit{{mu}}={}, \mathit{{Sigma}}={})$'.format(
name, get_variable_name(nu), get_variable_name(mu),
get_variable_name(Sigma))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
n = dist.n
p = dist.p
psi = dist.psi
return r'${} \sim \text{{ZeroInflatedBinomial}}(\mathit{{n}}={}, \mathit{{p}}={}, \mathit{{psi}}={})$'.format(name,
get_variable_name(n),
get_variable_name(p),
get_variable_name(psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
nu = dist.innov.nu
mu = dist.innov.mu
cov = dist.innov.cov
name = r"\text{%s}" % name
return r"${} \sim \text{MvStudentTRandomWalk}(\mathit{{nu}}={},~\mathit{{mu}}={},~\mathit{{cov}}={})$".format(
name, get_variable_name(nu), get_variable_name(mu),
get_variable_name(cov))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
w = dist.w
sd = dist.sd
return r'${} \sim \text{{NormalMixture}}(\mathit{{w}}={}, \mathit{{mu}}={}, \mathit{{sigma}}={})$'.format(name,
get_variable_name(w),
get_variable_name(mu),
get_variable_name(sd))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
omega = dist.omega
alpha_1 = dist.alpha_1
beta_1 = dist.beta_1
return r'${} \sim \text{GARCH}(1, 1, \mathit{{omega}}={}, \mathit{{alpha_1}}={}, \mathit{{beta_1}}={})$'.format(name,
get_variable_name(omega),
get_variable_name(alpha_1),
get_variable_name(beta_1))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
try:
cov = dist.cov
except AttributeErrir:
cov = dist.chol_cov
return r'${} \sim \text{{MvNormal}}(\mathit{{mu}}={}, \mathit{{cov}}={})$'.format(
name, get_variable_name(mu), get_variable_name(cov))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
N = dist.N
n = dist.n
k = dist.k
name = r'\text{%s}' % name
return r'${} \sim \text{{HyperGeometric}}(\mathit{{N}}={},~\mathit{{n}}={}, ,~\mathit{{k}}={})$'.format(
name, get_variable_name(N), get_variable_name(n),
get_variable_name(k))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
rt = dist.rt
location = dist.location
scale = dist.scale
name = r'\text{%s}' % name
return r'${} \sim \text{{King}}(\mathit{{loc}}={},\mathit{{scale}}={},\mathit{{tidal_radius}}={})$'.format(
name, get_variable_name(location), get_variable_name(scale),
get_variable_name(rt))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
nu = dist.nu
name_nu = get_variable_name(nu)
name_mu = get_variable_name(mu)
return (r'${} \sim \text{{MvStudentT}}'
r'(\mathit{{nu}}={}, \mathit{{mu}}={}, '
r'{})$'.format(name, name_nu, name_mu,
self._repr_cov_params(dist)))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
nu = dist.innov.nu
mu = dist.innov.mu
cov = dist.innov.cov
name = r'\text{%s}' % name
return r'${} \sim \text{MvStudentTRandomWalk}(\mathit{{nu}}={},~\mathit{{mu}}={},~\mathit{{cov}}={})$'.format(name,
get_variable_name(nu),
get_variable_name(mu),
get_variable_name(cov))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
nu = dist.nu
name_nu = get_variable_name(nu)
name_mu = get_variable_name(mu)
return (r'${} \sim \text{{MvStudentT}}'
r'(\mathit{{nu}}={}, \mathit{{mu}}={}, '
r'{})$'
.format(name, name_nu, name_mu, self._repr_cov_params(dist)))
def _repr_cov_params(self, dist=None):
if dist is None:
dist = self
if self._cov_type == 'chol':
chol = get_variable_name(self.chol)
return r'\mathit{{chol}}={}'.format(chol)
elif self._cov_type == 'cov':
cov = get_variable_name(self.cov)
return r'\mathit{{cov}}={}'.format(cov)
elif self._cov_type == 'tau':
tau = get_variable_name(self.tau)
return r'\mathit{{tau}}={}'.format(tau)
def _repr_cov_params(self, dist=None):
if dist is None:
dist = self
if self._cov_type == 'chol':
chol = get_variable_name(self.chol)
return r'\mathit{{chol}}={}'.format(chol)
elif self._cov_type == 'cov':
cov = get_variable_name(self.cov)
return r'\mathit{{cov}}={}'.format(cov)
elif self._cov_type == 'tau':
tau = get_variable_name(self.tau)
return r'\mathit{{tau}}={}'.format(tau)
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
nu = dist.nu
Sigma = dist.Sigma
name_nu = get_variable_name(nu)
name_mu = get_variable_name(mu)
name_sigma = get_variable_name(Sigma)
return (r'${} \sim \text{{MvStudentT}}'
r'(\mathit{{nu}}={}, \mathit{{mu}}={}, '
r'\mathit{{Sigma}}={})$'.format(name, name_nu, name_mu,
name_sigma))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
n = dist.n
p = dist.p
psi = dist.psi
name_n = get_variable_name(n)
name_p = get_variable_name(p)
name_psi = get_variable_name(psi)
return (r'${} \sim \text{{ZeroInflatedBinomial}}'
r'(\mathit{{n}}={}, \mathit{{p}}={}, '
r'\mathit{{psi}}={})$'.format(name, name_n, name_p, name_psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
omega = dist.omega
alpha_1 = dist.alpha_1
beta_1 = dist.beta_1
name = r"\text{%s}" % name
return r"${} \sim \text{GARCH}(1,~1,~\mathit{{omega}}={},~\mathit{{alpha_1}}={},~\mathit{{beta_1}}={})$".format(
name,
get_variable_name(omega),
get_variable_name(alpha_1),
get_variable_name(beta_1),
)
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
n = dist.n
p = dist.p
psi = dist.psi
name_n = get_variable_name(n)
name_p = get_variable_name(p)
name_psi = get_variable_name(psi)
return (r'${} \sim \text{{ZeroInflatedBinomial}}'
r'(\mathit{{n}}={}, \mathit{{p}}={}, '
r'\mathit{{psi}}={})$'
.format(name, name_n, name_p, name_psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
alpha = dist.alpha
psi = dist.psi
name_mu = get_variable_name(mu)
name_alpha = get_variable_name(alpha)
name_psi = get_variable_name(psi)
return (r'${} \sim \text{{ZeroInflatedNegativeBinomial}}'
r'(\mathit{{mu}}={}, \mathit{{alpha}}={}, '
r'\mathit{{psi}}={})$'
.format(name, name_mu, name_alpha, name_psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
alpha = dist.alpha
psi = dist.psi
name_mu = get_variable_name(mu)
name_alpha = get_variable_name(alpha)
name_psi = get_variable_name(psi)
return (r'${} \sim \text{{ZeroInflatedNegativeBinomial}}'
r'(\mathit{{mu}}={}, \mathit{{alpha}}={}, '
r'\mathit{{psi}}={})$'.format(name, name_mu, name_alpha,
name_psi))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
p = dist.p
name = r'\text{%s}' % name
return r'${} \sim \text{{Categorical}}(\mathit{{p}}={})$'.format(
name, get_variable_name(p))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
name = r'\text{%s}' % name
return r'${} \sim \text{{Poisson}}(\mathit{{mu}}={})$'.format(
name, get_variable_name(mu))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
gamma = dist.gamma
name = r'\text{%s}' % name
return r'${} \sim \text{{EFF}}(\mathit{{\gamma}}={})$'.format(
name, get_variable_name(gamma))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
scale = dist.scale
name = r'\text{%s}' % name
return r'${} \sim \text{{EDSD}}(\mathit{{scale}}={})$'.format(
name, get_variable_name(scale))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
dt = dist.dt
name = r'\text{%s}' % name
return r'${} \sim \text{EulerMaruyama}(\mathit{{dt}}={})$'.format(name,
get_variable_name(dt))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
p = dist.p
name = r'\text{%s}' % name
return r'${} \sim \text{{Categorical}}(\mathit{{p}}={})$'.format(name,
get_variable_name(p))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
mu = dist.mu
name = r'\text{%s}' % name
return r'${} \sim \text{{Poisson}}(\mathit{{mu}}={})$'.format(name,
get_variable_name(mu))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
dt = dist.dt
name = r'\text{%s}' % name
return r'${} \sim \text{EulerMaruyama}(\mathit{{dt}}={})$'.format(name,
get_variable_name(dt))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
p = dist.p
return r'${} \sim \text{{Geometric}}(\mathit{{p}}={})$'.format(
name, get_variable_name(p))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
a = dist.a
return r'${} \sim \text{{Dirichlet}}(\mathit{{a}}={})$'.format(name,
get_variable_name(a))
def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
p = dist.p
return r'${} \sim \text{{Geometric}}(\mathit{{p}}={})$'.format(name,
get_variable_name(p))